How can we classify an image using AI? Answer is in this video :)
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About a year ago, I started the pianist AI project with the aim of having an AI model that can generate piano pieces. Although the optimization is still in process, today, finally it seems the model has learned the basic concepts.
I have named the first piece of Level 7: Peace> https://youtu.be/rLW3KwCG41M
With the hope of better tomorrow….
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Deep generative models have produced realistic samples in a variety of domains, including image and audio. Video generation has recently emerged as the next issue for deep generative models, prompting a long line of research to learn video distribution.
Despite their efforts, there is still a big gap between large-scale real-world recordings and simulations. The intricacy of video signals, which are continuously coupled across spatiotemporal directions, contributes to the difficulty of video creation. Specifically, most previous works have modeled the video as a 3D grid of RGB values, i.e., a succession of 2D images, using discrete decoders such as convolutional or autoregressive networks. However, because of the cubic complexity, such discrete modeling limits the scalability of created movies and misses the intrinsic continuous temporal dynamics.
Continue Reading My Article Summary On This Research
Paper: https://openreview.net/pdf?id=Czsdv-S4-w9
Github: https://github.com/sihyun-yu/digan
Project: https://sihyun-yu.github.io/digan/
https://reddit.com/link/t7f7ti/video/nicll8ujvll81/player
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This is part 1 in a multi-part series on the value-realizing, collaborative power of decisions. My mom used to say, “If it was a snake, you’d be dead”. And the reason that I say that, is that organizations are seeking a collaborative value driver that can 1) align the organization around the economic power of… Read More »Decisions Part 1: Creating an AI-driven Decision Factory
The post Decisions Part 1: Creating an AI-driven Decision Factory appeared first on Data Science Central.
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Hello everyone. I am excited about the invitation to do an AMA here. It's my first AMA on reddit, and I will be trying my best! I recently wrote the "Machine Learning with Pytorch and Scikit-Learn" book and joined a startup(Grid.ai) in January. I am also an Assistant Professor of Statistics at the University of Wisconsin-Madison since 2018. Btw. I am also a very passionate Python programmer and love open source.
Please feel free to ask me anything about my book, working in industry (although my experience is still limited, haha), academia, or my research projects. But also don't hesitate to go on tangents and ask about other things -- this is an ask me anything after all (... topics like cross-country skiing come to mind).
EDIT:
Thanks everyone for making my first AMA here a really fun experience! Unfortunately, I have to call it a day, but I had a good time! Thanks for all the good questions, and sorry that I couldn't get to all of them!
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MEng graduate students engage with IBM to develop their research skills and solutions to real-world problems.
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Whether you’re allocating resources more efficiently for web traffic, forecasting patient demand for staffing needs, or anticipating sales of a company’s products, forecasting is an essential tool across many businesses. One particular use case, known as cold start forecasting, builds forecasts for a time series that has little or no existing historical data, such as […]
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Real estate businesses have existed for many years and will almost definitely continue to succeed.
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The deployment of powerful AI systems has enriched our understanding of safety and misuse far more than would have been possible through research alone. Notably:
API-based language model misuse often comes in different forms than we feared most.
We have identified limitations in existing language model evaluations that we are
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Call for expressions of interest to study the economic impacts of Codex.
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https://www.immunai.com/press/advancements-in-gene-regulatory-networks-immunais-third-symposium
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We have two talks next week (week of 7 Mar.) for our upcoming webinar series about the intersection of Bayesian inference and causal inference. Our speakers will help us understand how we can use these two frameworks in order to solve applied problems, and will consider if these different frameworks are in conflict or are complimentary. The intended audience is machine learning practitioners and statisticians from academia and industry.
Upcoming talks, with Zoom registration links:
7 March
Andrew Gelman - Bayesian Methods in Causal Inference and Decision Making
Consider the problem of A/B testing (that is, an experiment or observational study designed to estimate the effect of some exposure or treatment). The basic data analysis workflow is to start by comparing the average outcomes…
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A new MIT-wide effort launched by the Institute for Data, Systems, and Society uses social science and computation to address systemic racism.
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At the 2021 AWS re:Invent conference in Las Vegas, we demoed Read For Me at the AWS Builders Fair—a website that helps the visually impaired hear documents. For better quality, view the video here. Adaptive technology and accessibility features are often expensive, if they’re available at all. Audio books help the visually impaired read. Audio […]
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A new month means a whole new set of games coming to GeForce NOW. Members can look forward to 27 titles joining the GeForce NOW library in March, including day-and-date releases like Shadow Warrior 3, with support for NVIDIA DLSS. Bring a Katana to a Gunfight Shoot, slash and slide into Shadow Warrior 3, new Read article >
The post GFN Thursday Marches Forward With 27 Games Coming to GeForce NOW This Month appeared first on NVIDIA Blog.
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This is a beefed version of SEER which was released a year ago, scaled from 1B to 10B parameters, showing improved generalization on different tasks.
Also, the self supervised learning (SSL) allowed for a better coverage of the world , thus reducing bias from labelled datasets mostly originating from specific countries (e.g. US).
Results look very nice.
More details in their post.
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Hi folks, we recently implemented multi-touch attribution using shapley and markov chain values at our org, and I wrote a blog post about how we implemented it using a mix of tools (primarily dbt, sagemaker, and our internal tools). I am sharing it here hoping people might find it interesting. Do let me know if you have any questions/feedback/suggestions.
https://www.rudderstack.com/blog/from-first-touch-to-multi-touch-attribution-with-rudderstack-dbt-and-sagemaker/
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Are there any pytorch libraries to do benchmarking of domain adaptation methods for audio/speech tasks? Something like the Transfer Learning Library (https://github.com/thuml/Transfer-Learning-Library/) for images.
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Programming is way more fun when you learn/work with someone. Help each other, ask questions, brainstorm, etc. There is just so much benefit to joining a community when you are in this field, especially when you cannot find the question you are looking for on stack overflow! 😉
This is the same thing with AI, and it is why, nearly two years ago now, we created a Discord server where anyone learning or working in the field could come and share their projects, learn together, work together, and much more. The community has now over 22'000 members, which is just unbelievable! We are so glad to see it growing and especially to see everyone so active.
We would love for anyone to join and exchange with us, especially if you are willing to give some of your precious time to share your knowledge and help other people.
We have special events and projects for the community as well as cool offers and giveaways, such as an NVIDIA RTX 3080Ti giveaway running right now in collaboration with NVIDIA for the GTC event for the community members! (check out the #announcement channel for more information about this ;) )
Come join us if you are in the field of AI !
https://discord.gg/learnaitogether
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Imagine walking through the bustling streets of London’s Piccadilly Circus, when suddenly you’re in a tropical rainforest, surrounded by vibrant flowers and dancing butterflies. That’s what audiences will see in the virtual world of The Green Planet AR Experience, an interactive, augmented reality experience that blends physical and digital worlds to connect people with nature. Read article >
The post Beyond Be-leaf: Immersive 3D Experience Transports Audiences to Natural Worlds With Augmented Reality appeared first on NVIDIA Blog.
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The very thing that makes the internet so useful to so many people — the vast quantity of information that’s out there — can also make going online frustrating. There’s so much available that the sheer volume of choices can be overwhelming. That’s where recommender systems come in, explains NVIDIA AI Podcast host Noah Kravitz. Read article >
The post Podsplainer: What’s a Recommender System? NVIDIA’s Even Oldridge Breaks It Down appeared first on NVIDIA Blog.
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The Social and Ethical Responsibilities of Computing publishes a collection of original pedagogical materials developed for instructional use on MIT OpenCourseWare.
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Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes.
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In the presence of extrinsic rewards, Deep Reinforcement Learning (RL) is a strong strategy for tackling complex control tasks. Playing video games with pixels, mastering the game of Go, robotic mobility, and dexterous manipulation policies are all examples of successful applications.
While effective, the above advancements resulted in agents that were unable to generalize to new downstream tasks other than the one for which they were trained. Humans and animals, on the other hand, can learn skills and apply them to a range of downstream activities with little supervision. In a recent paper, UC Berkeley researchers aim to teach agents with generalization capabilities by efficiently adapting their skills to downstream tasks.
Continue reading my summary on this paper
Paper | Github
https://preview.redd.it/jeomp8g2d0l81.png?width=1370&format=png&auto=webp&s=24701cfc995eeffcace5a62fb734dce643499487
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What is Stroke ?
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The importance of set pieces in football (or soccer in the US) has been on the rise in recent years: now more than one quarter of all goals are scored via set pieces. Free kicks and corners generally create the most promising situations, and some professional teams have even hired specific coaches for those parts […]
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In football, as in many sports, discussions about individual players have always been part of the fun. “Who is the best scorer?” or “Who is the king of defenders?” are questions perennially debated by fans, and social media amplifies this debate. Just consider that Erling Haaland, Robert Lewandowski, and Thomas Müller alone have a combined […]
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How to utilize ML tools for gene regulatory networks and perturbation predictions: https://www.immunai.com/press/advancements-in-gene-regulatory-networks-immunais-third-symposium
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Veritasium posted a great video on the upcoming analog computers being used for neural networks as an alternative to digital computers.
https://www.youtube.com/watch?v=GVsUOuSjvcg&ab_channel=Veritasium
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Hi everyone!
I created RasgoQL as an open source python package for building dbt-compatible SQL in a pandas-like syntax. It’s already saved me hours of writing CTEs (common table expressions) in SQL, and I hope you’ll give it a try so it can save you time too.
You can check it out here: https://github.com/rasgointelligence/RasgoQL
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Synthetic datasets are computer-generated samples with the same statistical characteristics as the samples from the original dataset. Synthetic datasets are becoming common to train AIs in areas where real data is scarce or too sensitive to use, as in the case of medical records or personal financial data. I was involved in textual data augmentation for my thesis.
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Sponsored Post Me, a data scientist, and Jupyter notebooks. Well, our relationship started back then when I began to learn […]
The post Data Science Notebook Life-Hacks I Learned From Ploomber appeared first on Machine Learning Mastery.
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Serialization refers to the process of converting a data object (e.g. Python objects, Tensorflow models) into a format that allows […]
The post A Gentle Introduction to Serialization for Python appeared first on Machine Learning Mastery.
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The last few years have seen rapid development in the field of natural language processing (NLP). While hardware has improved, such as with the latest generation of accelerators from NVIDIA and Amazon, advanced machine learning (ML) practitioners still regularly run into issues scaling their large language models across multiple GPU’s. In this blog post, we […]
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Ambarella builds computer vision SoCs (system on chips) based on a very efficient AI chip architecture and CVflow that provides the Deep Neural Network (DNN) processing required for edge inferencing use cases like intelligent home monitoring and smart surveillance cameras. Developers convert models trained with frameworks (such as TensorFlow or MXNET) to Ambarella CVflow format […]
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Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial […]
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Can something like that be done? For example, the Kaluza-Klein theory was able to derive various existing physic equations using an 5 by 5 matrix. My idea comes from the first 4 minutes of this video https://www.youtube.com/watch?v=mmtLgYVEuJs
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GauGAN, an AI demo for photorealistic image generation, allows anyone to create stunning landscapes using generative adversarial networks. Named after post-Impressionist painter Paul Gauguin, it was created by NVIDIA Research and can be experienced free through NVIDIA AI Demos. How to Create With GauGAN The latest version of the demo, GauGAN2, turns any combination of Read article >
The post What Is GauGAN? How AI Turns Your Words and Pictures Into Stunning Art appeared first on NVIDIA Blog.
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Researchers surveyed 100 high-performing companies to determine which of them are leading adopters of machine intelligence and data analytics, and how they succeed.
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A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.
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For anyone curious. Announcement on Twitter: https://twitter.com/sirbayes/status/1498402522511253510
Download link: https://probml.github.io/pml-book/book2.html
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Hi, after months of closed beta I'm launching today a free, open source IDE for PyTorch called TorchStudio. It aims to greatly simplify researches and trainings with PyTorch and its ecosystem, so that most tasks can be done visually in a couple clicks. Hope you'll like it, I'm looking forward to feedback and suggestions :)
-> https://torchstudio.ai
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See here: https://cltc.berkeley.edu/reward-reports/
The authors propose a new kind of AI documentation, Reward Reports, that track what an RL system is optimizing for over time as it learns the dynamics of a domain and (as the case may be) actively reshapes them to conform to its specification.
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Every piece of software and code contains flaws. While some of these flaws are minor and simply impair an application’s functioning, others have the potential to compromise its security. It is important to find and remedy these security flaws for application security.
Code scanning is one such framework that now uses machine learning for detecting potential security flaws in software that identifies vulnerabilities and corrects them before they are released into production, reducing the security risks they offer. Continue reading our summary or you can also read Github blog
https://reddit.com/link/t37lvz/video/wsiqmbaxbik81/player
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Why do data silos, and now analytic silos, continue to exist? It can’t be due to technical issues. Data silos appeared in the 1990s when we were trying to make Relational Data Base Management Systems (RDMBS) – that were architected for single-record transaction processing – perform massive table scans to identify the trends, patterns, and… Read More »Abundance Mentality is Key to Exploiting the Economics of Data
The post Abundance Mentality is Key to Exploiting the Economics of Data appeared first on Data Science Central.
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When looking into Machine Learning models, I came across something that confuses me:
On the one hand "model" is referred to (e.g. by Google) as something you pick before you start the training process, meaning the raw mathematical method (e.g. linear regression).
On the other hand "model" is referred to (e.g. by Google) as the fully trained mathematical method, so the mapping from input to output that you get after training (e.g. the result of conducting a linear regression on the training data).
Is both correct? If not, how would you call the remaining thing from the two options above?
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https://www.youtube.com/watch?v=sKhCKWR8OwI&list=PLhCQIYxdniNtEto-CO33yVb0ah7yKOVV5&index=5
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AMA friday morning EST with Sebastian Raschka /u/seraschka author of Machine Learning with Pytorch and Scikit-Learn Book
It is on goodreads here
https://www.goodreads.com/book/show/60098440-machine-learning-with-pytorch-and-scikit-learn?from_search=true&from_srp=true&qid=MNIHuvctFr&rank=1
And a link to the code and such here
https://www.reddit.com/r/learnmachinelearning/comments/t1gqqe/machine_learning_with_pytorch_and_scikitlearn_book/
Ask him questions about his new book, academic research, or his job at http://Grid.ai
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See links attached for the two specifically the cpn results:
Poseformer - https://github.com/zczcwh/PoseFormer VideoPose - https://github.com/facebookresearch/VideoPose3D
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KServe (originally known as KFServing) provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It is now the latest Incubation Project of the LF AI & Data Foundation.
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Note: I have not, personally, used this package.
The package is developed by scientists at Argonne National lab.
It sounds very interesting and uses bayesian optimization to explore the parameter space for ML model configuration (no. of neurons, loss, optimizer etc).
Edit: link thanks to u/ploomber-io
https://github.com/deephyper/deephyper
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You can read about here implemented in TensorFlow 2.8, trained in tf.GradientTape() API.
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A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.
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Hi
We have a very friendly and supportive wellness bot called Hawai. You can share about everyday stuffs and get tips on feeling good and happy. Give it a shot! Spread the word, especially given that millions are affected and feel so isolated!!! You can help just by sharing in social media and among your friends!!! Thanks!!!
Start a chat with Hawai - Health and wellbeing AI on Chai
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https://thegradient.pub/gradient-prize/
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A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
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This is a guest post by Kustomer’s Senior Software & Machine Learning Engineer, Ian Lantzy, and AWS team Umesh Kalaspurkar, Prasad Shetty, and Jonathan Greifenberger. In Kustomer’s own words, “Kustomer is the omnichannel SaaS CRM platform reimagining enterprise customer service to deliver standout experiences. Built with intelligent automation, we scale to meet the needs of […]
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Predictive maintenance can be an effective way to prevent industrial machinery failures and expensive downtime by proactively monitoring the condition of your equipment, so you can be alerted to any anomalies before equipment failures occur. Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are the foundational elements for enabling predictive […]
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Amazon SageMaker is a managed service that makes it easy to build, train, and deploy machine learning (ML) models. Data scientists use SageMaker training jobs to easily train ML models; you don’t have to worry about managing compute resources, and you pay only for the actual training time. Data ingestion is an integral part of […]
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The intersection of DI and AI: Drug information refers to the discovery, use, and management of healthcare and medical information. Healthcare providers have many challenges associated with drug information discovery, such as intensive time involvement, lack of accessibility, and accuracy of reliable data. The average clinical query requires a literature search that takes an average of 18.5 hours. In addition, drug information often lies in disparate information silos, behind pay walls and design walls, and quickly becomes stale.
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We have three talks next week (week of 28 Feb.) for our upcoming webinar series about the intersection of Bayesian inference and causal inference. Our speakers will help us understand how we can use these two frameworks in order to solve applied problems, and will consider if these different frameworks are in conflict or are complimentary. The intended audience is machine learning practitioners and statisticians from academia and industry.
Upcoming talks, with Zoom registration links:
28 Feb.
Christopher Sims - Large Parameter Spaces and Weighted Data: A Bayesian Perspective
Bayesian analysis can suggest ignoring sampling weights, even in contexts where popular estimation methods like Horvitz-Thompson or “doubly robust” estimates do use the weights. Weights are sometimes treated in …
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Hello here 👋
I just posted the second blog post of a series about Sentiment Analysis: https://medium.com/besedo-engineering/sentiment-analysis-part-2-how-to-choose-pre-annotated-datasets-for-sentiment-analysis-d79736e8c147
This blog post is about how to choose the perfect dataset for your Sentiment Analysis project, which is one of the most important part of a project. I hope you'll like it! 🙌
Any feedback is appreciated 🙏
Bonus: There are still two blog posts on this series!
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https://www.youtube.com/watch?v=IGOuV6UyQ1Q&list=PLhCQIYxdniNuu422uTU91PRHoj3gxF0uD&index=3
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Hi All!
I was wondering what are some of the ways that people use to automate their notebook works?
The main challenges I had in mind are version control, monitoring, better collaboration within the team, standardization across teams, and fast cloud deployments.
Disclosure, I'm part of the Ploomber team (an open source framework) and it solves most if not all of those problems.
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The Data Centric approach to building AI systems focuses on data instead of code.
We thought it would be cool if there was a central repository of all things Data Centric AI, so we set out to build one. We have put together a list of research papers and open-source tools on Data Centric AI, that we think you will find useful.
https://mindkosh.com/data-centric-ai/research-papers.html
https://mindkosh.com/data-centric-ai/open-source-tools.html
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According to the New-York Times, using machine learning, stylometry, and statistics on Q texts, two separate teams of NLP researchers from France and Swiss have identified the same two men as likely authors of messages that fueled the QAnon movement. First the initiator, Paul Furber, a South African software developer and then Ron Watkins took over, who operated 8chan website where the Q messages began appearing in 2018 and is now running election for Republican in Arizona.
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The development of ecologically acceptable biochemical substitutes for industrial processes might be accelerated thanks to nature’s molecular machinery.
Enzymes are the master accelerators of nearly every activity in the human body, helping with everything from digestion to the breakdown of hazardous chemicals and even DNA replication. Enzymes’ relevance extends beyond biology; they’re also utilized to make industrial chemical processes more environmentally friendly by reducing energy consumption and the number of harmful solvents needed in their production. The enzyme Xylanase treatment in paper manufacture, for example, has been demonstrated to reduce chlorine usage by 15% and toxic adsorbable organic halides (a chlorine byproduct) by 25% when producing white paper for printing or use in notebooks.
You can continue reading this article and tell us your feedback.
Github: https://github.com/rxn4chemistry/biocatalysis-model
Project: https://rxn.res.ibm.com/
Paper: https://www.nature.com/articles/s41467-022-28536-w.pdf
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Humans have the fundamental cognitive ability to perceive the environment through multimodal sensory signals and utilize this to accomplish a wide variety of tasks. It is crucial that an autonomous agent can similarly perceive the underlying state of an environment from different sensors and appropriately consider how to accomplish a task. For example, localization (or […]
The post COMPASS: COntrastive Multimodal Pretraining for AutonomouS Systems appeared first on Microsoft Research.
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Within the Mogao Caves, a cultural crossroads along what was the Silk Road in northwestern China, lies a natural reserve of tens of thousands of historical documents, paintings and statues of the Buddha.
The post Meet the Omnivore: 3D Creator Makes Fine Art for Digital Era Inspired by Silk Road Masterpieces appeared first on NVIDIA Blog.
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There’s no question that bad data hurts the bottom line. Bad customer data costs companies six percent of their total sales, according to a UK Royal Mail survey. The UK Government’s Data Quality Hub estimates organizations spend between 10% and 30% of their revenue tackling data quality. For multi-billion-dollar companies, that can easily be hundreds of millions of dollars… Read More »Data Observability Goes Far Beyond Data Quality Monitoring and Alerts
The post Data Observability Goes Far Beyond Data Quality Monitoring and Alerts appeared first on Data Science Central.
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Why do data silos, and now analytic silos, continue to exist? It can’t be due to technical issues. Data silos appeared in the 1990s when we were trying to make Relational Data Base Management Systems (RDMBS) – that were architected for single-record transaction processing – perform massive table scans to identify the trends, patterns, and… Read More »Abundance Mentality is Key to Exploiting the Economics of Data
The post Abundance Mentality is Key to Exploiting the Economics of Data appeared first on Data Science Central.
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As the demands of the modern market continue to evolve, more and more companies are starting to realize that their current infrastructure is not suited to keep up with the market and its requirements. And cloud management refers to the exercise of control over public, private, or hybrid cloud infrastructure with the right resources and… Read More »Top Best Practices to Keep in Mind for Azure Cloud Migration
The post Top Best Practices to Keep in Mind for Azure Cloud Migration appeared first on Data Science Central.
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An important issue for people developing ML-Models:
These biases affect belief formation, reasoning processes, business and economic decisions, and human behavior in general.
I've compiled a list (pdf) of over 150 biases (mainly from Wikipedia). Maybe this is useful for some.
The pdf can be downloaded for free here: A List of over 150 Biases (Belief, decision-making & behavioral, Social, Memory) (Medium)
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I just included 3 anecdotal stories in this article about machine learning model misinterpretation. Do you have any stories you've heard about model misinterpretation?
https://medium.com/codex/why-you-should-think-critically-about-your-machine-learning-model-outputs-864bc7d80709
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Hey guys, below I compiled a list of some relatively successful ML companies which also use various components of blockchain technology (aka cryptography). Feel free to comment on other companies which I might have left out:
https://www.linkedin.com/posts/xs94_ai-blockchain-machinelearning-activity-6901922373159710721-s_d_
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video!
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In this video, someone mentioned that he thinks self-supervised learning could solve RL problems. And on his Facebook page, he had some posts that look like RL memes.
What do you think?
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With more than 11,000 stores across Thailand serving millions of customers, CP All, the country’s sole licensed operator of 7-Eleven convenience stores, recently turned to AI to dial up its call centers’ service capabilities. Built on the NVIDIA conversational AI platform, the Bangkok-based company’s customer service bots help call-center agents answer frequently asked questions and Read article >
The post Talking the Talk: Retailer Uses Conversational AI to Help Call Center Agents Increase Customer Satisfaction appeared first on NVIDIA Blog.
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This is, without a doubt, the best time to jump into cloud gaming. GeForce NOW RTX 3080 memberships deliver up to 1440p resolution at 120 frames per second on PC, 1600p and 120 FPS on Mac, and 4K HDR at 60 FPS on NVIDIA SHIELD TV, with ultra-low latency that rivals many local gaming experiences. Read article >
The post How to Make the Most of GeForce NOW RTX 3080 Cloud Gaming Memberships appeared first on NVIDIA Blog.
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A well-popularized article in Quanta magazine about a scientific paper presented last December at NeurIPS, by Sébastien Bubeck of Microsoft Research and Mark Sellke of Stanford University provided a mathematical proof of why overparametrization (neural networks with more parameters than the number of training samples) is necessary to big neural network to learn well. The short answer is ROBUSTNESS...
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A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
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Hi there, many of you have probably been aware of the whole twitter drama about AI consciousness, but if not you may find this write up about it interesting - Neural nets are not "slightly conscious," and AI PR can do with less hype . It's mostly a recap, but it does include a bunch of fun meme replies to the whole thing that you might enjoy even if you're aware of this whole thing.
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We posted our VAD demo here a while ago. Here's a follow-up article on The Gradient, where we attempt to explain:
Which values we did pursue;
Why we decided to create our own VAD;
Which criteria and metrics we optimized;
A brief overview of what is available in general;
How it compares with well-established and similar class solutions.
Links:
The article
The VAD is always available on Github
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Autonomous checkout is a rapidly advancing technology that has the potential to transform the way people shop in physical establishments. It frequently uses cameras and other sensors to gain a sense of a shopping environment and make a final conclusion about what a buyer buys. In systems where cameras are the only sensors available, computer vision is critical for comprehending this data. While vision-only autonomous checkout is still a relatively new concept, no benchmarks or new tasks have emerged.
In a recent study, researchers from Stony Brook University and Standard Cognition hypothesized that understanding retail settings necessitates not only domain-specific data but also a new computer vision task that detects changes in retail sceneries over time. Thus, they provide a new dataset, StandardSim, as well as a novel goal for detecting changes in retail scenarios over time in this study.
Quick Read: https://www.marktechpost.com/2022/02/19/stony-brook-university-researchers-introduce-standardsim-a-large-scale-photorealistic-synthetic-dataset-featuring-annotations-for-semantic-segmentation-instance-segmentation-depth-estimation-a/
Paper: https://arxiv.org/pdf/2202.02418.pdf
Github: https://github.com/standard-ai/Standard-Sim
Project: https://standard-ai.github.io/Standard-Sim/
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Currently I use TensorflowOnSpark frame to train and predict model. When prediction, I have billions of samples to predict which is time-consuming. I wonder if there is some good practices on this.
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When video chatting with colleagues, coworkers, or family, many of us have grown accustomed to using virtual backgrounds and background filters. It has been shown to offer more control over the surroundings, allowing fewer distractions, preserving the privacy of those around us, and even liven up our virtual presentations and get-togethers. However, Background filters don’t always work as expected or perform well for everyone.
Image segmentation is a computer vision process of separating the different components of a photo or video. It has been widely used to improve backdrop blurring, virtual backgrounds, and other augmented reality (AR) effects. Despite advanced algorithms, achieving highly accurate person segmentation seems challenging. Continue Reading
Meta Source: https://ai.facebook.com/blog/creating-better-virtual-backdrops-for-video-calling-remote-presence-and-ar/
https://reddit.com/link/swkjkr/video/o57elp9gzui81/player
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So one of the clubs in my college has done something quite impossible by roping in Sir Geoffrey Hinton, a living legend! Would be great if y'all join in, it's actually kinda a one-time opportunity to interact with him live, so do not miss it!
Link for RSVP
Youtube Link
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https://www.youtube.com/watch?v=IGOuV6UyQ1Q&list=PLhCQIYxdniNuu422uTU91PRHoj3gxF0uD&index=3
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Engineers build a lower-energy chip that can prevent hackers from extracting hidden information from a smart device.
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Release notes are here: https://github.com/openai/gym/releases/tag/0.22.0
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Hi all,
Tensorboard is a nice tool to visualize experiment results. However, it is quite difficult to parse the event logs into raw data for scientific plotting. So, I've created a PyPI package to make the parsing process as simple as possible (2 lines of code) and can be installed from pip: pip install tbparse. It supports reading event files generated by PyTorch/TensorFlow/Keras/TensorboardX and can parse most of the event types supported by tensorboard.
The package is open source (https://github.com/j3soon/tbparse) and the usages are documented in detail (https://tbparse.readthedocs.io/en/latest/). My friends and I have been using it for a while and find it very convenient, so I think some of you may benefit from it. I would be happy to hear your feedback and feature requests.
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What's the difference between the terms "metaheuristic" vs machine learning?
The wiki for metaheuristic articulates many similar ideas in machine learning yet I don't often see explicit connections between the two in literature.
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Hi, I'm fairly new to reinforcement learning. Can anyone help me to identify the reinforcement learning algorithm used in the following project?
I've tried and couldn't identify it. Any help is appreciated TIA.
This is the GitHub link for the project code
This is the link for the agent in the same.
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Guinness World Records this week presented a Stanford University-led research team with the first record for fastest DNA sequencing technique — a benchmark set using a workflow sped up by AI and accelerated computing. Achieved in five hours and two minutes, the DNA sequencing record can allow clinicians to take a blood draw from a Read article >
The post Guinness World Record Awarded for Fastest DNA Sequencing — Just 5 Hours appeared first on The Official NVIDIA Blog.
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I've been experimenting with GauGAN2, released in Nov 2021 as a follow-on to GauGAN. One new thing Nvidia added in GauGAN2 is the ability to generate a picture to match a phrase.
"A rocky stream in an ancient mossy rainforest"
It can do more than
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AI Weirdness: the strange side of machine learning
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This model classifies the different types of stars by means of artificial intelligence using Neural Designer.
https://www.neuraldesigner.com/learning/examples/star-type
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I wanted to share this blogpost on a recent paper of mine. It talks a bit through getting deep reinforcement learning to learn on a piece of hardware. If you have any questions left on the practicalities of reinforcement learning, feel free to AMA! https://www.deepmind.com/blog/article/Accelerating-fusion-science-through-learned-plasma-control
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Hey,
Is there any intuition around when we would want to learn std dev as a layer connected to our learned latent space vs as a separate Parameter space?
In some PPO implementations where the policy outputs a tensor of normal distributions (e.g. in continuous output spaces), sometimes the std dev is a learned parameter but it is not a function of the input, e.g. https://github.com/openai/spinningup/blob/038665d62d569055401d91856abb287263096178/spinup/algos/pytorch/ppo/core.py#L85
In other cases, the core network will output both the mean + std.
Thanks!
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A team of neuroscientists, engineers, and physicians showed a machine learning system for constantly automating propofol administration in a special issue of Artificial Intelligence in Medicine. The algorithm outperformed more traditional software in sophisticated, physiology-based simulations of patients using an application of deep reinforcement learning.
The software’s neural networks simultaneously learned how to maintain unconsciousness and critique the efficacy of their own actions. It also nearly matched genuine anesthesiologists’ performance when demonstrating what it would take to maintain unconsciousness given data from nine actual procedures.
The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed. Hence, freeing up anesthesiologists for all of the other responsibilities in the operating room, such as ensuring patients remain immobile, experience no pain, remain stable, and receive adequate oxygen. Continue Reading
Paper: https://www.sciencedirect.com/science/article/pii/S0933365721002207?via%3Dihub
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I am currently reading a paper on UAV Mapping System for Agricultural Field Surveying and was curious on what AI technologies does it use (e.g., model-based diagnosis, belief networks,semantic networks, heuristic search, constraint satisfaction search, regression) or something else??
And also why is it intelligent or what aspect makes it intelligent?.
Link to paper: https://www.mdpi.com/1424-8220/17/12/2703/htm
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A team of neuroscientists, engineers, and physicians showed a machine learning system for constantly automating propofol administration in a special issue of Artificial Intelligence in Medicine. The algorithm outperformed more traditional software in sophisticated, physiology-based simulations of patients using an application of deep reinforcement learning.
The software’s neural networks simultaneously learned how to maintain unconsciousness and critique the efficacy of their own actions. It also nearly matched genuine anesthesiologists’ performance when demonstrating what it would take to maintain unconsciousness given data from nine actual procedures.
The algorithm’s advances increase the feasibility for computers to maintain patient unconsciousness with no more drug than is needed. Hence, freeing up anesthesiologists for all of the other responsibilities in the operating room, such as ensuring patients remain immobile, experience no pain, remain stable, and receive adequate oxygen. Continue Reading
Paper: https://www.sciencedirect.com/science/article/pii/S0933365721002207?via%3Dihub
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In the last two decades, the web and digital technologies have become inseparable parts of business operations globally. It is not only just using the internet and applications for enhancing workflow; businesses are relying on advanced and customised software tailor-made for their needs. They are also using diverse types of web-based services to reach out… Read More »5 Easy Steps to Choosing a Great Data Visualization Platform for Your Business
The post 5 Easy Steps to Choosing a Great Data Visualization Platform for Your Business appeared first on Data Science Central.
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Real-time rendering and photorealistic graphics used to be tall tales, but NVIDIA Omniverse has made them fact from fiction. NVIDIA’s own artists are writing new chapters in Omniverse, an accelerated 3D design platform that connects and enhances 3D apps and creative workflows, to showcase these stories. Combined with the NVIDIA Studio platform, Omniverse and Studio-validated Read article >
The post Bringing Novel Idea to Life, NVIDIA Artists Create Retro Writer’s Room in Omniverse With ‘The Storyteller’ appeared first on The Official NVIDIA Blog.
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GeForce NOW’s RTX 3080 membership is the next generation of cloud gaming. This GFN Thursday looks at one of the tier’s major benefits: ultra-low-latency streaming from the cloud. This week also brings a new app update that lets members log in via Discord, a members-only World of Warships reward and eight titles joining the GeForce Read article >
The post Performance You Can Feel: Putting GeForce NOW RTX 3080 Membership’s Ultra-Low Latency to the Test This GFN Thursday appeared first on The Official NVIDIA Blog.
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An introduction to Artificial neural networks
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In the previous article I have talked a lot about deep learning, neural networks, and types of them but today in this article we will learn…
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In many practical areas of machine learning, such as explainability, feature selection, data valuation, ensemble pruning, and federated learning, measuring relevance and attribution of various gains is a crucial topic.
For example, one may wonder: How important is a certain feature in a machine learning model’s decisions? What is the value of a single data point? Which models in an ensemble are the most valuable? Specific ways have been used to solve these concerns in various sectors. Continue Reading
Paper: https://arxiv.org/pdf/2202.05594v1.pdf
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Hi, i am happy to share with you the edited version of cityscapes to foggy cityscapes dataset for unsupervised domain adaptation ready to be used =)
https://github.com/fpv-iplab/Cityscapes-FoggyCityscapes
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Yesss.... A first paper in Nature today: Magnetic control of tokamak plasmas through deep reinforcement learning. After the proteins folding breakthrough, Deepmind is tackling controlled fusion through deep reinforcement learning (DRL). With the long-term promise of abundant energy without greenhouse gas emissions. What a challenge! But Deemind's Google's folks, you are our heros! Do it again! A Wired popular article.
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Scale released an interesting blogpost recently, looks like anyone can just log in and start using Scale Rapid to get data labels. Also thought the use case with the anime CycleGAN was pretty cool and had some interesting results!
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Hi ML community,
we recently released all of our papers regarding ML / AI federation, scaling and multi-processing over our website: https://www.databloom.ai/science
Its free, and we are happy to answer questions. We are also the team behind Apache Wayang, if you want to contribute, we are also happy!
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Hi all,
I am interested in finding references to up-to-date evaluation methods for generative modeling in unsupervised tasks across different model types.
I am familiar with papers such as https://arxiv.org/abs/2002.09797, https://arxiv.org/abs/2102.08921, https://arxiv.org/abs/1806.00035, which are motivated by being unable to estimate the likelihood or a lower-bound on the likelihood when using GANs.
Suppose one hoped to compare the performance across GANs, flows, and VAEs in a particular scenario. Would one of the above references, or something similar, be an approach you would consider?
Also, say you ignore GANs and compare models such as flows, VAEs, and other latent variable models. Would you consider similar approaches? I understand these models involve likelihood estimation or estimating a lower bound on the likelihood, but considering it is a lower bound for some of these models, comparing these seems off.
I appreciate any help you can provide.
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The textbook is published in print format, but a pdf version (recent draft) is available as a pdf.
Link: https://probml.github.io/pml-book/book1.html
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This post is co-authored with Jan Paul Assendorp, Thomas Lietzow, Christopher Masch, Alexander Meinert, Dr. Lars Palzer, Jan Schillemans of SIGNAL IDUNA. At SIGNAL IDUNA, a large German insurer, we are currently reinventing ourselves with our transformation program VISION2023 to become even more customer oriented. Two aspects are central to this transformation: the reorganization of […]
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Real-time feedback helps drive learning. This is especially important for designing presentations, learning new languages, and strengthening other essential skills that are critical to succeed in today’s workplace. However, many students and lifelong learners lack access to effective face-to-face instruction to hone these skills. In addition, with the rapid adoption of remote learning, educators are […]
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Time series data is widely present in our lives. Stock prices, house prices, weather information, and sales data captured over time are just a few examples. As businesses increasingly look for new ways to gain meaningful insights from time-series data, the ability to visualize data and apply desired transformations are fundamental steps. However, time-series data […]
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Easily one of the most revolutionary technologies in recent times — Blockchain is slated to disrupt so many industries that its…
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We are surrounded by artificial intelligence (AI) and machine learning (ML). Both AI and machine learning have altered the way we live and…
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Jaguar Land Rover and NVIDIA are redefining modern luxury, infusing intelligence into the customer experience. As part of its Reimagine strategy, Jaguar Land Rover announced today that it will develop its upcoming vehicles on the full-stack NVIDIA DRIVE Hyperion 8 platform, with DRIVE Orin delivering a wide spectrum of active safety, automated driving and parking Read article >
The post Reimagining Modern Luxury: NVIDIA Announces Partnership with Jaguar Land Rover appeared first on The Official NVIDIA Blog.
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Stepping deeper into the era of exascale AI, Atos gave the first look at its next-generation high-performance computer. The BullSequana XH3000 combines Atos’ patented fourth-generation liquid-cooled HPC design with NVIDIA technologies to deliver both more performance and energy efficiency. Giving users a choice of Arm or x86 computing architectures, it will come in versions using Read article >
The post Atos Previews Energy-Efficient, AI-Augmented Hybrid Supercomputer appeared first on The Official NVIDIA Blog.
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Python is a duck typing language. It means the data types of variables can change as long as the syntax […]
The post Duck-typing, scope, and investigative functions in Python appeared first on Machine Learning Mastery.
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We live in a complex world that is full of data, and it’s getting even more full every day. In 2020, the world collectively created, captured, copied, and consumed nearly 64.2 zettabytes of data and by 2025 that figure is expected to more than double to 180 zettabytes. Increasingly, companies depend on this data to… Read More »Calling All Data Scientists: Data Observability Needs You
The post Calling All Data Scientists: Data Observability Needs You appeared first on Data Science Central.
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https://thereader.mitpress.mit.edu/the-staggering-ecological-impacts-of-computation-and-the-cloud/
This supports many of the points made in this: https://kv-emptypages.blogspot.com/2021/11/the-carbon-footprint-of-machine-learning.html
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https://github.com/JoaoLages/RATransformers
I have made a package to be able to use pretrained language models on structured data.
By changing self-attention to be relation aware, you are able to pass implicit relations within the input to the model.
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Hey r/ml! I thought people here might enjoy (or possibly have a great discussion about) the latest episode in the MLOps Podcast.
In this episode, I'm speaking with Laszlo Sragner about how data scientists can write better code, how it affects real-world ML projects, and how to build an ML team. We also talk about how to break down ML problems into smaller, more manageable tasks, and a bunch of other things.
You can watch it here: https://www.youtube.com/watch?v=mtwGV-x3nSM
or listen to it here, or read some of the Q&A.
Would love to open up a discussion – what are your best practices for improving code-craft in machine learning projects?
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Now get all official and unofficial code implementations of any AI/ML papers as you're browsing DuckDuckGo, Reddit, Google, Scholar, Arxiv, Twitter and more!
(if code not yet available, request it with 1-click as well!)
https://chrome.google.com/webstore/detail/aiml-papers-with-code-eve/aikkeehnlfpamidigaffhfmgbkdeheil
https://preview.redd.it/zpkx0j1fdwh81.png?width=1265&format=png&auto=webp&s=59f732f9d26223ba20ba4958b389a50617b27f06
https://preview.redd.it/mn656t1fdwh81.png?width=1265&format=png&auto=webp&s=cb6d7d3ddce53cc6c2113e41e80f2d2acd0be964
https://preview.redd.it/0ertru1fdwh81.png?width=1167&format=png&auto=webp&s=a0f5df0905c7dc873ec702c564290cbe9bd7adb7
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This model classifies iris flowers among three species (Setosa, Versicolor or Virginica) based on the length and width measurements of the sepals and petals using Neural Designer
https://www.neuraldesigner.com/learning/examples/iris-flowers-classification
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At the latest UEFA Champions League Finals, one of the world’s most anticipated annual soccer events, pop stars Marshmello, Khalid and Selena Gomez shared the stage for a dazzling opening ceremony at Portugal’s third-largest football stadium — without ever stepping foot in it. The stunning video performance took place in a digital twin of the Read article >
The post Peak Performance: Production Studio Sets the Stage for Virtual Opening Ceremony at European Football Championship appeared first on The Official NVIDIA Blog.
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A new deep-learning algorithm trained to optimize doses of propofol to maintain unconsciousness during general anesthesia could augment patient monitoring.
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Internet of things (IoT) holds an enormous promise in making urban transport systems smarter in terms of safety, energy-saving, ecologically favorable, and efficiency. The efficiency of optimizing transportation in real-time is the key pillar of successful deployment of IoT. Ecosystem to Develop Pivoted on Expanding Use Cases Several developed nations notably Singapore, the U.S., and… Read More »IoT in Intelligent Transportation Systems Anchors Smart Traffic for Smart Cities
The post IoT in Intelligent Transportation Systems Anchors Smart Traffic for Smart Cities appeared first on Data Science Central.
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Amazon SageMaker Autopilot makes it possible for organizations to quickly build and deploy an end-to-end machine learning (ML) model and inference pipeline with just a few lines of code or even without any code at all with Amazon SageMaker Studio. Autopilot offloads the heavy lifting of configuring infrastructure and the time it takes to build […]
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Architecture evaluation is a systematic approach for identifying flaws and dangers in architectural designs. The evaluation process is ideally performed before they are implemented.
Typically, neural architecture search (NAS) systems are used for architectural evaluation. Neural architecture search (NAS) is an AutoML branch that aims to find the best deep-learning model architecture for a task. The systems achieve this by finding an architecture that will achieve the best performance metric on the given task dataset and search space of possible architectures. However, this usually necessitates training each proposed model completely on the dataset, which takes a long time. Continue Reading
Paper: http://proceedings.mlr.press/v139/xu21m/xu21m.pdf
Github: https://github.com/Jingjing-NLP/KNAS
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Datacenter accelerators are pieces of hardware that are specifically built to process visual data. It’s a physical device or software program that boosts a computer’s overall performance. Continuous advancements in creating and delivering data center (DC) machine learning (ML) accelerators, such as TPUs and GPUs, have proven crucial for scaling up contemporary ML models and applications. These upgraded accelerators’ ultimate performance (e.g., FLOPs) is orders of magnitude higher than that of standard computing systems.
However, there is a rapidly widening gap between the potential peak performance supplied by state-of-the-art hardware and the actual achievable performance when ML models run on these kinds of hardware. Continue Reading
Paper: https://openaccess.thecvf.com/content/CVPR2021/papers/Li\_Searching\_for\_Fast\_Model\_Families\_on\_Datacenter\_Accelerators\_CVPR\_2021\_paper.pdf
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Looking for a challenge? Try maneuvering a Kenyan minibus through traffic or dropping seed balls on deforested landscapes. Or download Africa’s Legends and battle through fiendishly difficult puzzles with Ghana’s Ananse or Nigeria’s Oya by your side. Games like these are connecting with a hyper-connected African youth population that’s growing fast. Africa is the youngest Read article >
The post New Levels Unlocked: Africa’s Game Developers Reach Toward the Next Generation appeared first on The Official NVIDIA Blog.
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Hey all!
We're building towards a GPT3 level moment in computer vision, and here's our v0 - https://youtu.be/P7zcc8iZ0YA
This v0 runs on 13B parameters, with 18B and 34B model iterations coming in the pipeline.
Access to the model is gated as of now to help us monitor scale, you can sign up at - https://banana-dev.typeform.com/carrot
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